Huggingface stable diffusion

This model card focuses on the model associated with the Stable Diffusion v2 model, available here. This stable-diffusion-2 model is resumed from stable-diffusionbase base-ema. Resumed for another k steps on x images, huggingface stable diffusion.

Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI announcement and our own blog post for more technical details. You can find the original codebase for Stable Diffusion v1. Explore these organizations to find the best checkpoint for your use-case! The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:.

Huggingface stable diffusion

Stable Video Diffusion SVD is a powerful image-to-video generation model that can generate second high resolution x videos conditioned on an input image. This guide will show you how to use SVD to generate short videos from images. Before you begin, make sure you have the following libraries installed:. To reduce the memory requirement, there are multiple options that trade-off inference speed for lower memory requirement:. Stable Diffusion Video also accepts micro-conditioning, in addition to the conditioning image, which allows more control over the generated video:. Diffusers documentation Stable Video Diffusion. Get started. Overview Understanding pipelines, models and schedulers AutoPipeline Train a diffusion model Load LoRAs for inference Accelerate inference of text-to-image diffusion models. Using Diffusers. Overview Load pipelines, models, and schedulers Load and compare different schedulers Load community pipelines and components Load safetensors Load different Stable Diffusion formats Load adapters Push files to the Hub. Overview Unconditional image generation Text-to-image Image-to-image Inpainting Text or image-to-video Depth-to-image.

If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here.

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more detailed instructions, use-cases and examples in JAX follow the instructions here. Follow instructions here. Model Description: This is a model that can be used to generate and modify images based on text prompts. Resources for more information: GitHub Repository , Paper. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

Getting the DiffusionPipeline to generate images in a certain style or include what you want can be tricky. This tutorial walks you through how to generate faster and better with the DiffusionPipeline. One of the simplest ways to speed up inference is to place the pipeline on a GPU the same way you would with any PyTorch module:. To make sure you can use the same image and improve on it, use a Generator and set a seed for reproducibility :. By default, the DiffusionPipeline runs inference with full float32 precision for 50 inference steps. You can speed this up by switching to a lower precision like float16 or running fewer inference steps. Another option is to reduce the number of inference steps. Choosing a more efficient scheduler could help decrease the number of steps without sacrificing output quality. You can find which schedulers are compatible with the current model in the DiffusionPipeline by calling the compatibles method:. The easiest way to see how many images you can generate at once is to try out different batch sizes until you get an OutOfMemoryError OOM.

Huggingface stable diffusion

Why is this important? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a x image being encoded to x Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a x image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable Diffusion 1. Therefore, this kind of model is well suited for usages where efficiency is important.

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The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. Note : If you are limited by TPU memory, please make sure to load the FlaxStableDiffusionPipeline in bfloat16 precision instead of the default float32 precision as done above. This affects the overall output of the model, as white and western cultures are often set as the default. This includes, but is not limited to: Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. During training, Images are encoded through an encoder, which turns images into latent representations. Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. However, the community has found some nice tricks to improve the memory constraints further. The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an improved aesthetics estimator. By far most of the memory is taken up by the cross-attention layers. You can do so by telling diffusers to expect the weights to be in float16 precision:. Experimental Features. We aim at generating a beautiful photograph of an old warrior chief and will later try to find the best prompt to generate such a photograph. While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

We present SDXL, a latent diffusion model for text-to-image synthesis.

Impersonating individuals without their consent. You can find which schedulers are compatible with the current model in the DiffusionPipeline by calling the compatibles method:. Stable Diffusion v Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Reinforcement Learning Audio Other Modalities. Research on generative models. Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. The same strategy was used to train the 1. Model Description: This is a model that can be used to generate and modify images based on text prompts. Collaborate on models, datasets and Spaces. Effective and efficient diffusion. This checker works by checking model outputs against known hard-coded NSFW concepts. Applications in educational or creative tools. Additionally, the community has started fine-tuning many of the above versions on certain styles with some of them having an extremely high quality and gaining a lot of traction. Training Procedure Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder.